Data validation with the pointblank package

This uses pointblank to create a data validation report. In the resulting table at the end, any failing tests should have a CSV button that lets you download a .csv file of just the rows of data that don’t pass that particular validation step.

Check missing values

Action levels

By default, warn if 1 or more rows fail conditions and error if 2% or more fail. Some checks are run with a stricter action level that errors if any rows fail.

al_default <-  action_levels(warn_at = 1, stop_at = 0.02) #warn if even row fails, error if 2% of rows fail
al_strict <- action_levels(stop_at = 1) #error if even one row fails

Data Validation

The two datasets being submitted with the data paper are HDP_plots.csv and HDP_1997_2009.csv

Checks for data type, range, and duplicates

Pointblank Validation
Data Validation tibbleWARN 1 STOP 0.02 NOTIFY —
		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
NA	1	col_vals_in_set()	▮subplot	A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, C1, C2, C3, C4, C5, C6, C7, C8, C9, C10, D1, D2, D3, D4, D5, D6, D7, D8, D9, D10, E1, E2, E3, E4, E5, E6, E7, E8, E9, E10, F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, G1, G2, G3, G4, G5, G6, G7, G8, G9, G10, H1, H2, H3, H4, H5, H6, H7, H8, H9, H10, I1, I2, I3, I4, I5, I6, I7, I8, I9, I10, J1, J2, J3, J4, J5, J6, J7, J8, J9, J10		✓	67K	67K 1	0 0	—	○	—	—
NA	2	col_vals_in_set()	▮plot	CF-1, CF-2, CF-3, CF-4, CF-5, CF-6, FF-1, FF-2, FF-3, FF-4, FF-5, FF-6, FF-7		✓	67K	67K 1	0 0	—	○	—	—
NA	3	Height is measured to nearest cm col_vals_expr()		ht%%1 == 0		✓	57K	57K 1	0 0	—	○	—	—
NA	4	Shoots is interger col_vals_expr()		shts%%1 == 0		✓	57K	57K 1	0 0	—	○	—	—
NA	5	Number of inflorescences is integer col_vals_expr()		infl%%1 == 0		✓	2K	2K 1	0 0	—	○	—	—
NA	6	shoots between 0 and 20 col_vals_between()	▮shts	[0, 20]		✓	67K	67K 1	8 0	●	○	—	CSV
NA	7	height between 0 and 200cm col_vals_between()	▮ht	[0, 200]		✓	67K	67K 1	2 0	●	○	—	CSV
NA	8	infloresences between 0 and 3 col_vals_between()	▮infl	[0, 3]		✓	67K	67K 1	15 0	●	○	—	CSV
NA	9	duplicated rows rows_distinct()		NA		✓	67K	67K 1	0 0	—	○	—	—
NA	10	col_vals_not_null()	▮plant_id	NA		✓	67K	67K 1	0 0	—	○	—	—
NA	11	Check for duplicate ID's within each year rows_distinct()	▮plant_id	NA		✓	3K	3K 1	0 0	—	○	—	—
NA	12	Check for duplicate ID's within each year rows_distinct()	▮plant_id	NA		✓	4K	4K 1	0 0	—	○	—	—
NA	13	Check for duplicate ID's within each year rows_distinct()	▮plant_id	NA		✓	5K	5K 1	0 0	—	○	—	—
NA	14	Check for duplicate ID's within each year rows_distinct()	▮plant_id	NA		✓	6K	6K 1	0 0	—	○	—	—
NA	15	Check for duplicate ID's within each year rows_distinct()	▮plant_id	NA		✓	6K	6K 1	0 0	—	○	—	—
NA	16	Check for duplicate ID's within each year rows_distinct()	▮plant_id	NA		✓	6K	6K 1	0 0	—	○	—	—
NA	17	Check for duplicate ID's within each year rows_distinct()	▮plant_id	NA		✓	6K	6K 1	0 0	—	○	—	—
NA	18	Check for duplicate ID's within each year rows_distinct()	▮plant_id	NA		✓	6K	6K 1	0 0	—	○	—	—
NA	19	Check for duplicate ID's within each year rows_distinct()	▮plant_id	NA		✓	7K	7K 1	0 0	—	○	—	—
NA	20	Check for duplicate ID's within each year rows_distinct()	▮plant_id	NA		✓	5K	5K 1	0 0	—	○	—	—
NA	21	Check for duplicate ID's within each year rows_distinct()	▮plant_id	NA		✓	6K	6K 1	0 0	—	○	—	—
NA	22	Check for duplicate ID's within each year rows_distinct()	▮plant_id	NA		✓	6K	6K 1	0 0	—	○	—	—
2022-12-06 17:18:14 UTC 4.7 s 2022-12-06 17:18:18 UTC

Year to year change

Checks that year to year change in size is reasonable

Pointblank Validation
Check growth & regression tibbleWARN 1 STOP 0.02 NOTIFY —
		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
NA	1	|% change in height| < 200% col_vals_lt()	▮ht_pc	2		✓	67K	66K 1	422 0	●	○	—	CSV
NA	2	|∆ height| < 100cm col_vals_between()	▮ht_diff	[−100, 100]		✓	67K	67K 1	11 0	—	●	—	CSV
NA	3	|∆ shoot number| < 5 col_vals_between()	▮shts_diff	[−5, 5]		✓	67K	67K 1	201 0	—	●	—	CSV
2022-12-06 17:18:22 UTC < 1 s 2022-12-06 17:18:22 UTC

Seedlings

Check that size of seedlings is reasonable

Pointblank Validation
Check seedlings tibbleWARN 1 STOP 0.02 NOTIFY —
		STEP	COLUMNS	VALUES	TBL	EVAL	UNITS	PASS	FAIL	W	S	N	EXT
NA	1	shoots < 3 col_vals_lt()	▮shts	3		✓	3K	3K 1	12 0	●	○	—	CSV
NA	2	height < 30cm col_vals_lt()	▮ht	30		✓	3K	3K 1	3 0	●	○	—	CSV
2022-12-06 17:18:24 UTC < 1 s 2022-12-06 17:18:24 UTC